This document investigates all differentially expressed genes from across contrast groups. The goal here is to analyze if genes are recurring in across contrast groups, which groups they are recurring in and attempt to functionally characterize them.

Compare differentially expressed gene groups gene groups

The Upsetplot below shows the amount of genes unique to each contrast and the overlaps between all the various groups.

The data table below it marks the diffexp status of all the genes found here for manual query, incase you want to know for a specific gene if it is deemed as differentially expressed across multiple groups.

## [1] "No enrichments present"
## [1] "No enrichments present"

Gene set enrichment

Since we cannot score genes between samples, we use over representation tests for the different categories shown the the upset plot above. For each group we attempt to run the over representation test and present the results across all comparisons classes shown in the upset plot above.

Each tab group shows the contrast analyed, in case no plot is shown, then no enrichments against msigdb could be found.

## [1] "AEC-vs-YEC + YECplusA-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC + YECplusA-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA"
## [1] "AEC-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC + YECplusA-vs-YEC + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA"
## [1] "AEC-vs-YEC + AECplusA-vs-YECplusA + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC + AEC-vs-YECplusA + AECplusA-vs-YEC"
## [1] "YECplusA-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA"
## [1] "AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC + YECplusA-vs-YEC"
## [1] "AEC-vs-YEC + AEC-vs-YECplusA"
## [1] "AEC-vs-YEC + AECplusA-vs-YEC"
## [1] "YECplusA-vs-YEC + AECplusA-vs-YECplusA"
## [1] "YECplusA-vs-YEC + AEC-vs-YECplusA"
## [1] "YECplusA-vs-YEC + AECplusA-vs-YEC"
## [1] "AECplusA-vs-YECplusA + AEC-vs-YECplusA"
## [1] "AECplusA-vs-YECplusA + AECplusA-vs-YEC"
## [1] "AEC-vs-YEC"
## [1] "YECplusA-vs-YEC"
## [1] "AECplusA-vs-YECplusA"
## [1] "AEC-vs-YECplusA"
## [1] "AECplusA-vs-YEC"

AEC-vs-YEC + YECplusA-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC H set

AEC-vs-YEC + YECplusA-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA H set

AEC-vs-YEC + AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC H set

AEC-vs-YEC + AEC-vs-YECplusA + AECplusA-vs-YEC H set

AECplusA-vs-YECplusA + AEC-vs-YECplusA + AECplusA-vs-YEC H set

AEC-vs-YEC + YECplusA-vs-YEC H set

AEC-vs-YEC + AECplusA-vs-YEC H set

AECplusA-vs-YECplusA + AEC-vs-YECplusA H set

AEC-vs-YECplusA H set

Overview of the Data cohort QC etc.

The following section gives a general overview of the data cohort.

Heatmap of all Diffexp genes

The following heatmap shows the expression levels of all differntially expressed genes. The gene expression is shown as normalized deseq2 rlog expression values.

Distance Plot

Euclidean sample distance from rlog2 transformed expression values. The distance matrix over all genes is calculated and plotted as a heatmap. Further rows and columns are clustered based on the euclidean distance. A clear split between the young and aged samples can be seen in the data. However, the gene expression between the young and young-treated samples seems to be higher than between the aged mouse ECs.

PCA

A PCA is made to better characterize the data. The first scree plot shows the amount of variation that is explained in each principal component. The barplots show the variation explained per pc, the red line shows the cumlative variance explained.

The first PCA plot of PC1 and PC2 shown here shows that the primary source of variance in the data stems from the young vs aged samples. PC2 seems to split young-EC from young-ec treated with apelin, but fails to do so in the aged samples, hinting that the affect of apelin may differ between these cells.

The second group of plots give an overview of the first 5 PCs, but no clear splits can be identified in the smaller PCs.